def test_soiling_srr_with_precip(normalized_daily, insolation, times):
precip = pd.Series(index=times, data=0)
precip['2019-01-18 00:00:00-07:00'] = 1
precip['2019-02-20 00:00:00-07:00'] = 1
kwargs = {'reps': 10, 'precipitation_daily': precip}
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily,
insolation,
clean_criterion='precip_and_shift',
**kwargs)
assert 0.983270 == pytest.approx(sr, abs=1e-6),\
"Soiling ratio with clean_criterion='precip_and_shift' different from expected"
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily,
insolation,
clean_criterion='precip_or_shift',
**kwargs)
assert 0.973228 == pytest.approx(sr, abs=1e-6),\
"Soiling ratio with clean_criterion='precip_or_shift' different from expected"
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily,
insolation,
clean_criterion='precip',
**kwargs)
assert 0.976196 == pytest.approx(sr, abs=1e-6),\
"Soiling ratio with clean_criterion='precip' different from expected"
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily,
insolation,
clean_criterion='shift',
**kwargs)
assert 0.963133 == pytest.approx(sr, abs=1e-6),\
"Soiling ratio with clean_criterion='shift' different from expected"
def test_soiling_srr_method(normalized_daily, insolation):
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily,
insolation,
reps=10,
method='random_clean')
assert 0.918767 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio with method="random_clean" different from expected value'
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily,
insolation,
reps=10,
method='perfect_clean')
assert 0.965653 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio with method="perfect_clean" different from expected value'
def test_soiling_srr_clean_threshold(normalized_daily, insolation):
'''Test that clean test_soiling_srr_clean_threshold works with a float and
can cause no soiling intervals to be found'''
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily,
insolation,
reps=10,
clean_threshold=0.01)
assert 0.963133 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio with specified clean_threshold different from expected value'
with pytest.raises(NoValidIntervalError):
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily,
insolation,
reps=10,
clean_threshold=0.1)
def test_soiling_srr_dayscale(normalized_daily, insolation):
'Test that a long dayscale can prevent valid intervals from being found'
with pytest.raises(NoValidIntervalError):
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily,
insolation,
confidence_level=68.2,
reps=10,
day_scale=90)
def test_soiling_srr_recenter_false(normalized_daily, insolation):
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily,
insolation,
reps=10,
recenter=False)
assert 1 == soiling_info['renormalizing_factor'],\
'Renormalizing factor != 1 with recenter=False'
assert 0.965158 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio different than expected when recenter=False'
def test_soiling_srr_trim(normalized_daily, insolation):
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily,
insolation,
reps=10,
trim=True)
assert 0.978369 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio with trim=True different from expected value'
assert len(soiling_info['soiling_interval_summary']) == 1,\
'Wrong number of soiling intervals found with trim=True'
def test_soiling_srr_max_negative_slope_error(normalized_daily, insolation):
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily,
insolation,
reps=10,
max_relative_slope_error=50.0)
assert list(soiling_info['soiling_interval_summary']['valid'].values) == [True, True, False],\
'Soiling interval validity differs from expected when max_relative_slope_error=50.0'
assert 0.952995 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio different from expected when max_relative_slope_error=50.0'
def test_soiling_srr_confidence_levels(normalized_daily, insolation):
'Tests SRR with different confidence level settingsf from above'
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily,
insolation,
confidence_level=95,
reps=10,
exceedance_prob=80.0)
assert np.array([0.957272, 0.964763]) == pytest.approx(sr_ci, abs=1e-6),\
'Confidence interval with confidence_level=95 different than expected'
assert 0.961285 == pytest.approx(soiling_info['exceedance_level'], abs=1e-6),\
'soiling_info["exceedance_level"] different than expected when exceedance_prob=80'
def test_soiling_srr_negative_step(normalized_daily, insolation):
stepped_daily = normalized_daily.copy()
stepped_daily.iloc[37:] = stepped_daily.iloc[25:] - 0.1
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(stepped_daily, insolation, reps=10)
assert list(soiling_info['soiling_interval_summary']['valid'].values) == [True, False, True],\
'Soiling interval validity differs from expected when a large negative step\
is incorporated into the data'
assert 0.934927 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio different from expected when a large negative step is incorporated into the data'
def test_soiling_srr_with_nan_interval(normalized_daily, insolation, times):
'''
Previous versions had a bug which would have raised an error when an entire interval
was NaN. See https://github.com/NREL/rdtools/issues/129
'''
reps = 10
normalized_corrupt = normalized_daily.copy()
normalized_corrupt[26:50] = np.nan
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_corrupt,
insolation,
reps=reps)
assert 0.947416 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio different from expected value when an entire interval was NaN'
def test_soiling_srr(normalized_daily, insolation, times):
reps = 10
np.random.seed(1977)
sr, sr_ci, soiling_info = soiling_srr(normalized_daily,
insolation,
reps=reps)
assert 0.963133 == pytest.approx(sr, abs=1e-6),\
'Soiling ratio different from expected value'
assert np.array([0.961054, 0.964019]) == pytest.approx(sr_ci, abs=1e-6),\
'Confidence interval different from expected value'
assert 0.958292 == pytest.approx(soiling_info['exceedance_level'], abs=1e-6),\
'Exceedance level different from expected value'
assert 0.984079 == pytest.approx(soiling_info['renormalizing_factor'], abs=1e-6),\
'Renormalizing factor different from expected value'
assert len(soiling_info['stochastic_soiling_profiles']) == reps,\
'Length of soiling_info["stochastic_soiling_profiles"] different than expected'
assert isinstance(soiling_info['stochastic_soiling_profiles'], list),\
'soiling_info["stochastic_soiling_profiles"] is not a list'
# Check soiling_info['soiling_interval_summary']
expected_summary_columns = [
'start', 'end', 'slope', 'slope_low', 'slope_high',
'inferred_start_loss', 'inferred_end_loss', 'length', 'valid'
]
actual_summary_columns = soiling_info[
'soiling_interval_summary'].columns.values
for x in actual_summary_columns:
assert x in expected_summary_columns,\
"'{}' not an expected column in soiling_info['soiling_interval_summary']".format(x)
for x in expected_summary_columns:
assert x in actual_summary_columns,\
"'{}' was expected as a column, but not in soiling_info['soiling_interval_summary']".format(x)
assert isinstance(soiling_info['soiling_interval_summary'], pd.DataFrame),\
'soiling_info["soiling_interval_summary"] not a dataframe'
expected_means = pd.Series({
'slope': -0.002617290,
'slope_low': -0.002828525,
'slope_high': -0.002396639,
'inferred_start_loss': 1.021514,
'inferred_end_loss': 0.9572880,
'length': 24.0,
'valid': 1.0
})
expected_means = expected_means[[
'slope', 'slope_low', 'slope_high', 'inferred_start_loss',
'inferred_end_loss', 'length', 'valid'
]]
pd.testing.assert_series_equal(
expected_means,
soiling_info['soiling_interval_summary'].mean(),
check_exact=False,
check_less_precise=6)
# Check soiling_info['soiling_ratio_perfect_clean']
pd.testing.assert_index_equal(
soiling_info['soiling_ratio_perfect_clean'].index,
times,
check_names=False)
assert 0.967170 == pytest.approx(soiling_info['soiling_ratio_perfect_clean'].mean(), abs=1e-6),\
"The mean of soiling_info['soiling_ratio_perfect_clean'] differs from expected"
assert isinstance(soiling_info['soiling_ratio_perfect_clean'], pd.Series),\
'soiling_info["soiling_ratio_perfect_clean"] not a pandas series'